The Empty Scorecard and the Fabrication Trap: The Data Discipline of a Vietnamese Table Tennis Analyst

At the Trinh Hoai Duc arena, in game five of a national table tennis championship men's singles semifinal, my screen showed a ten-cell spreadsheet. Three cells were filled. Seven were blank. It was the tightest game of the season — the score stretched to 12-10, every point required two serve changes, and the stands practically rose for the final rally. But the organizers' scoring software had stopped syncing from game three. My data column, the instrument I use to reconstruct the flow of a match, suddenly became a blank page. I held a semifinal complete in emotion but empty in number. And that moment — standing before an empty dataset, not a full one — is the moment that defines an analyst.

What would a newcomer do? They would fill the gap. They would write that 'player A won 60% of points in the decisive phase' with no basis whatsoever, because a sentence sounds far more professional when a number stands behind it. That is the trap I have seen more than once in twelve years of watching Vietnam's sports media. And that is the subject of this piece: what happens when data disappears, and why admitting 'I don't know' is far harder than inventing a round number.

Context: a table tennis scene rich in emotion, poor in data infrastructure

Vietnamese table tennis is an interesting paradox. On the emotional level it is extraordinarily rich: national championship finals fill the arena, clashes between leading players such as Nguyen Anh Tu, Dinh Quang Linh and Tran Tuan Quynh carry their own pull, and table tennis is one of the sports where Vietnam has won medals at regional level. But on the data-infrastructure level, we are at an embryonic stage.

Try to look up one simple fact: what was the serve-point win rate of Vietnam's number one player last season. In developed table tennis nations such as China, Japan or Germany this data sits on official statistical platforms, updated per tournament, even per match. In Vietnam you must go hunting — and in most cases you will not find it. Not because the data does not exist, but because it was never collected systematically, never standardized, and never made public.

Based on my own experience tracking matches over the years, I can state this: most table tennis statistics quoted by Vietnamese media originate from manual note-taking, compiled after the match, and usually stop at the per-game score. That means when an article says 'player X is in high form', it has almost no quantitative basis beyond the win-loss result.

This is the starting point for every issue this article wants to dissect. When data infrastructure is weak, the pressure to produce engaging content pushes writers toward unconscious fabrication. And unconscious fabrication — not deliberate lying — is the most dangerous enemy of honesty in sports analysis.

Four pillars of an honest dataset

From my own experience I draw four principles that anyone analyzing table tennis, professional or amateur, should follow. These four pillars are not idle theory; they are what I remind myself of whenever I sit before a spreadsheet.

Pillar one: a null value is a value. In the language of data people, we distinguish clearly between 'zero' and 'no data'. Zero means we measured and found nothing. 'No data' means we have not measured yet. The two are entirely different, and conflating them is a fatal error.

In table tennis this distinction matters enormously. If a player scores no points from topspin serves in a game, that is a zero — and it tells a tactical story. But if the organizers did not record that serve type, then it is 'no data' — and it tells no story except the story of a gap in collection. Filling a blank cell with zero, or worse with a guessed figure, turns a technical gap into a false conclusion. Numbers never lie; only the people reading them fool themselves — but a blank cell filled carelessly fools the reader before they even begin to read.

Pillar two: source transparency. Every number must answer: where did it come from, how was it measured, and who is responsible for it. In Vietnam's table tennis market this is especially hard because most circulating figures are 'digital hearsay' — one number appears in an article, then is cited by others, until no one remembers its true origin. I once tracked an average rally-length figure for a tournament repeated verbatim across four different articles, while none explained how it was calculated. It turned out a reporter had hand-counted two matches, then it was used as if it represented the whole event.

Pillar three: choosing the right metrics. Table tennis has its own metric system, different from football or basketball. The four core groups I favour are: serve-point win rate, receive-point win rate, average points per rally, and performance in the decisive phase (from point eight onward). Combined, these four build a player's tactical portrait far more clearly than the final score.

Take an example. A player wins 3-0 and it sounds dominant. But if we learn their serve-point win rate was only 48%, while their receive-point win rate reached 62%, the story is entirely different: they did not win through the power of their serves, but through reading and counter-attacking from the return. Conversely, another player with a 70% serve-point win rate who still loses narrowly in the deciding game is likely over-reliant on the serve, and once the opponent adjusts there is no fallback plan.

In football there is xG — expected goals — to separate result from chance quality. In table tennis the closest equivalent is the point-quality group of metrics, because a point can come from a dominant rally or from a simple opponent error. Without separating these two types of points we misprice true strength. A player winning 11-5 may have taken only two points from proactive rallies, the rest from opponent errors. Look at the score and they seem convincing; look at point quality and the picture is far more fragile.

Pillar four: humility before limits. This is the hardest pillar, because it runs against a writer's instinct to conclude. Data is the confession of those who once trusted feeling — but data has its limits too, and a good analyst is one who can say 'with the data available I can only conclude this far, the rest I am not sure about'. In a sport where samples are often small (a tournament has only a few dozen matches, a player contests only a few dozen games a year), over-concluding from small samples is a common error.

Three questions before trusting a number

From those four pillars I derive three check questions I set myself before using any figure in my writing.

First: over how many matches was this number measured? If the answer is 'one match', it is anecdotal data, not statistical data. Anecdotes can illustrate a point but cannot be the foundation for generalization. I once read an analysis claiming a young player 'tends to collapse at decisive points' based on exactly three points in one game. Three points are not enough to say anything about a career's tendency.

Second: has this number been cut from its context? A 65% serve-point win rate sounds impressive, but if that player only faced opponents a tier below their level, then the figure reflects the strength gap, not technical quality. Context is part of the number, not decoration attached to it.

Third: if this number is wrong, what happens? This is the question I cherish most, because it forces me to think about consequences. A skewed number can lead to a player being undervalued, a coach unfairly criticized, or a strategic decision made on a false premise. When we are aware of consequences, we become more careful with our sources and methods.

Applied in practice: the seven blank cells in my spreadsheet

Back to that afternoon at the Trinh Hoai Duc arena. My spreadsheet had seven blank cells. The only honest handling was to keep those seven cells blank and note clearly why they were empty. That meant my analysis of the semifinal would be shorter, less satisfying, and unable to deliver the strong tactical conclusions I usually do. But it would be honest.

Here is the irony: precisely because the data was empty, my writing became more honest. Had the software worked perfectly, I might have unconsciously overlooked anomalies, or priced a player on a short form streak without checking the sample. When data disappears, I am forced back to what I truly know: what I saw, what I recorded, and what I do not know.

In a market where most circulating information is rumour and emotion, this attitude is not highly valued. Readers want decisive answers. Sponsors want engaging stories. Organizers want pretty numbers. No one wants to hear 'we do not have enough data to conclude'. But it is precisely those moments of admitting shortfall that build an analyst's long-term credibility.

I have seen the opposite. Some colleagues, under pressure to publish immediately after a match, produced baseless figures. At first readers did not notice. But when a player or coach spoke out, their credibility collapsed fast. Reputation can be lost in a night, but a data model stays. Yet a data model only stays if built on real bricks — not on blank cells filled carelessly.

The counterintuitive angle: when the silence of data is itself data

But wait. Before concluding that every blank cell is bad, look from the other side. In some cases the very absence of data is a valuable signal.

Imagine a young player with no official statistics for an entire season. Traditionally this is an infrastructure problem. But from an analytical angle it is a question: why was this player not recorded? Perhaps they competed in small events, were never broadcast, and no organizer collected data. What does that say about the structure of the tournament system, about the gap between leading players and those below? The silence of data here is itself data — it exposes inequality in visibility.

Similarly, when I see a player loudly praised by media with no metric to prove it, that too is a signal. It tells me the media story is built on emotion, and I should check more carefully before believing any claim about them. The gap between the story told and the number that never appeared is always where I pay most attention.

This is the key counterintuitive point: an analyst's goal is not to fill every blank, but to understand why they are blank. A perfect dataset can hide more serious problems than an incomplete one. When every metric looks beautiful, when every player has full statistics, we should ask who collected them, by what standard, and whether some inconvenient numbers were dropped along the way. Excessive perfection in sports data is often a sign of selection, not completeness.

And here I must guard against myself. Form is an illusion; only the string of numbers is the real current — but the string of numbers can also be another illusion if it is selected to tell a predetermined story. Data-school analysts like me are often tempted to believe numbers are absolutely objective. They are not. Numbers are chosen by measurement method, by timing, by presentation. Between the two poles — pure emotion and pure data — there is always a space of judgment no formula can fill.

Signal for the next round

So what needs to change? I believe Vietnamese table tennis needs a minimum standard for match data, agreed between organizers, the federation and the media. Nothing complex. Just the four basic metric groups I named, consistently collected and published. With that standard, writers and readers alike know where they stand — and blank cells become a clean signal of what has not been measured, rather than an excuse to invent a number.

To those writing about table tennis, I want to say one thing: keep the blank cell. Do not fill it with a pretty figure. Because an honest analysis with seven empty cells is more useful than an analysis stuffed with numbers none of which can be trusted. When the whole world shouts emotion, a writer may choose to listen to the chart — but must also be brave enough to say that this time, the chart is silent.